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Disaster recovery ensures systems can restore services and data after major disruptions.

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

RPO and RTO are key metrics for planning disaster recovery strategies.

Replicas keep your system available, while snapshots allow for data recovery.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Choosing between single and multi-region architecture affects system resilience and cost.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

High Availability ensures your application remains operational even during server failures.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

Active-passive architecture ensures a standby system takes over if the main system fails.

Data residency is crucial for compliance with laws about where data can be stored.

Graceful degradation allows apps to function partially during service failures.

Fallbacks allow systems to handle failures safely and maintain user experience.

Fault domains help prevent multiple servers from failing together in an architecture.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

A single component's failure can take down your entire application, even with redundancy.

Backup strategies include Full, Incremental, and Differential, each with unique benefits.

Partial failure means some services fail while others keep running, impacting system reliability.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Kafka consumers resume processing from the last committed offset after a crash.

Failover ensures systems remain operational by managing leader changes during failures.

Availability Zones help prevent downtime by isolating failures across multiple locations.

N+1 redundancy means having one extra server to maintain capacity during a failure.

Crash failures stop a service, while network failures disrupt communication.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

Active-Active Architecture uses multiple regions to serve traffic simultaneously for better resilience.

Availability is about access; reliability is about correct performance.

Database replication helps keep data available and allows systems to handle more read requests.

Cross-region database replication keeps databases synchronized across different geographical locations.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Write-Ahead Log ensures data is safely recorded before changes are made.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Database replication enhances data availability and read performance but has challenges like replication lag.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Retry storms occur when too many retries overload a failing service.

Kafka consumer reprocessing involves safely replaying events to correct system states.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

Cassandra uses a masterless architecture to distribute and replicate data efficiently.

Kafka lag recovery time is how fast consumers can process backlogged messages.

You can replay Kafka events safely by separating live and replay processes.

Classifying failures and using controlled retries in Kafka prevents outages.

Retention policies help manage how long data is kept in databases.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Late-arriving events can be dropped, updated, or routed based on system needs.

RPC allows one service to call a function in another service over the network.

Container data can be lost unless stored in persistent volumes.

Load shedding helps systems reject excess requests to maintain performance during high demand.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka uses replication and in-sync replicas to ensure data is always available.

Multi-region data consistency ensures that users in different locations see the same data.

Zero Trust Architecture ensures that no user or device is trusted by default.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.